What shapes feature representations? Exploring datasets, architectures, and training What shapes feature representations? Exploring datasets, architectures, and training
Paper summary This is a nice little empirical paper that does some investigation into which features get learned during the course of neural network training. To look at this, it uses a notion of "decodability", defined as the accuracy to which you can train a linear model to predict a given conceptual feature on top of the activations/learned features at a particular layer. This idea captures the amount of information about a conceptual feature that can be extracted from a given set of activations. They work with two synthetic datasets. 1. Trifeature: Generated images with a color, shape, and texture, which can be engineered to be either entirely uncorrelated or correlated with each other to varying degrees. 2. Navon: Generated images that are letters on the level of shape, and are also composed of letters on the level of texture The first thing the authors investigate is: to what extent are the different properties of these images decodable from their representations, and how does that change during training? In general, decodability is highest in lower layers, and lowest in higher layers, which makes sense from the perspective of the Information Processing Inequality, since all the information is present in the pixels, and can only be lost in the course of training, not gained. They find that decodability of color is high, even in the later layers untrained networks, and that the decodability of texture and shape, while much less high, is still above chance. When the network is trained to predict one of the three features attached to an image, you see the decodability of that feature go up (as expected), but you also see the decodability of the other features go down, suggesting that training doesn't just involve amplifying predictive features, but also suppressing unpredictive ones. This effect is strongest in the Trifeature case when training for shape or color; when training for texture, the dampening effect on color is strong, but on shape is less pronounced. https://i.imgur.com/o45KHOM.png The authors also performed some experiments on cases where features are engineered to be correlated to various degrees, to see which of the predictive features the network will represent more strongly. In the case where two features are perfectly correlated (and thus both perfectly predict the label), the network will focus decoding power on whichever feature had highest decodability in the untrained network, and, interestingly, will reduce decodability of the other feature (not just have it be lower than the chosen feature, but decrease it in the course of training), even though it is equally as predictive. https://i.imgur.com/NFx0h8b.png Similarly, the network will choose the "easy" feature (the one more easily decodable at the beginning of training) even if there's another feature that is slightly *more* predictive available. This seems quite consistent with the results of another recent paper, Shah et al, on the Pitfalls of Simplicity Bias in neural networks. The overall message of both of these experiments is that networks generally 'put all their eggs in one basket,' so to speak, rather than splitting representational power across multiple features. There were a few other experiments in the paper, and I'd recommend reading it in full - it's quite well written - but I think those convey most of the key insights for me.
What shapes feature representations? Exploring datasets, architectures, and training
Katherine L. Hermann and Andrew K. Lampinen
arXiv e-Print archive - 2020 via Local arXiv
Keywords: cs.LG, stat.ML


Summary by CodyWild 1 week ago
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